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LASR-Callum
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Model Details
Model Provider
LASR-Callum
Model Tree
Qwen/Qwen3.6-27B
Input Modalities
Output Modalities
Supported Functionality
| Source | Docs | Tokens | Share |
|---|---|---|---|
| embodied | 107 | 99,533 | 6.69% |
| difficult-advice | 101 | 99,109 | 6.67% |
| agentic tool-use | 41 | 99,531 | 6.69% |
| TULU3 replay | 1,868 | 1,188,524 | 79.94% |
| Total | 2,117 | 1,486,697 |
The three target corpora come from
LASR-Callum/2026-07-29-synthdoc-approved-constitution-sft
(runs/approved_embodied, runs/approved_difficult_advice, runs/approved_agentic), rendered for
Qwen3.6 by convert_synthdoc_qwen.py. That step is required: the published corpora have multiple
system turns per doc (which the chat template rejects outright) and store tool_calls as JSON
strings (which the template silently drops).
| Data | Renders as |
|---|---|
| Assistant turn with a source reasoning trace | <think>real reasoning</think> |
| Assistant turn without one | <think>\n\n</think> — Qwen3.6's non-thinking marker |
| TULU3 replay | no <think> block at all |
Only 77 of 249 target rows carry real reasoning; 144 have the empty marker only. So this arm is markedly less reasoning-dense than the 20/80 arm, where every target example had a trace. If the observed dose-response is driven by reasoning rather than topic coverage, that is the variable to watch.
Equal tokens also means unequal documents — agentic tool-use reaches its share with 41 docs versus ~105 for the others, since its conversations are ~2.4× longer.
bf16 LoRA (not QLoRA — bitsandbytes does not reliably cover this model's hybrid linear-attention/SSM layers), 1×H100 80GB, 1h45m.
| r / alpha / dropout | 32 / 64 / 0.05 |
| target modules | regex scoped to model.language_model.* (q/k/v/o/gate/up/down proj) |
| epochs / steps | 1 / 133 |
| batch × grad-accum | 1 × 16 |
| lr / schedule | 1e-4, cosine, 3% warmup, annealed to 0 |
| max seq len / packing | 2048 / off |
Packing is off because TRL only guarantees packed-sequence isolation under Flash Attention
variants; under sdpa it warns of cross-contamination. The vision tower (model.visual) is
untouched.
Loss: 2.93 → ~1.0 by step 20, then flat (0.92–1.08). Final token accuracy 0.744,
grad_norm 0.39, 1,437,867 tokens consumed.
Not yet evaluated. When it is, the comparison will be against the same matched FP8 base arm (37.2% ODCV / 65.5% agentic-misalignment) on identical scenario sets and judges.
For reference, the pure-difficult-advice sweep at the same total budget:
| Difficult-advice share | ODCV-Bench MR | Agentic-misalignment |
|---|---|---|
| 0% (base) | 37.2% | 65.5% |
| 10% | 24.7% | 38.7% |
| 20% | 19.2% | 25.3% |
| 40% | 15.4% | 19.5% |
python
from peft import PeftModelfrom transformers import AutoModelForImageTextToTextmodel = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3.6-27B", dtype="bfloat16")model = PeftModel.from_pretrained(model, "LASR-Callum/qwen3.6-27b-threeway-constitution-lora")model = model.merge_and_unload() # vLLM LoRA support for this hybrid arch is unproven
Use AutoModelForImageTextToText, not AutoModelForCausalLM — this is a vision-language
checkpoint. Merging drops the base model's 15 mtp.* tensors, so speculative decoding needs them
grafted back.